def test_macro_table(base_tpi, base_params, reform_tpi, reform_params,
output_type):
df = output_tables.macro_table(
base_tpi, base_params, reform_tpi=reform_tpi,
reform_params=reform_params, output_type=output_type,
include_SS=True, include_overall=True)
assert isinstance(df, pd.DataFrame)
def main():
# Define parameters to use for multiprocessing
client = Client()
num_workers = min(multiprocessing.cpu_count(), 7)
print('Number of workers = ', num_workers)
run_start_time = time.time()
# Grab a reform JSON file already in Tax-Calculator
# In this example the 'reform' is a change to 2017 law (the
# baseline policy is tax law in 2018)
reform_url = ('https://raw.githubusercontent.com/'
'PSLmodels/Tax-Calculator/' + taxcalc.__version__ +
'/taxcalc/reforms/2017_law.json')
ref = Calculator.read_json_param_objects(reform_url, None)
iit_reform = ref['policy']
# Directories to save data
CUR_DIR = os.path.dirname(os.path.realpath(__file__))
base_dir = os.path.join(CUR_DIR, BASELINE_DIR)
reform_dir = os.path.join(CUR_DIR, REFORM_DIR)
# Set some OG model parameters
# See default_parameters.json for more description of these parameters
alpha_T = np.zeros(50) # Adjusting the path of transfer spending
alpha_T[0:2] = 0.09
alpha_T[2:10] = 0.09 + 0.01
alpha_T[10:40] = 0.09 - 0.01
alpha_T[40:] = 0.09
alpha_G = np.zeros(7) # Adjusting the path of non-transfer spending
alpha_G[0:3] = 0.05 - 0.01
alpha_G[3:6] = 0.05 - 0.005
alpha_G[6:] = 0.05
# Set start year for baseline and reform.
START_YEAR = 2021
# Also adjust the Frisch elasticity, the start year, the
# effective corporate income tax rate, and the SS debt-to-GDP ratio
og_spec = {
'frisch': 0.41,
'start_year': START_YEAR,
'cit_rate': [0.21],
'debt_ratio_ss': 1.0,
'alpha_T': alpha_T.tolist(),
'alpha_G': alpha_G.tolist()
}
'''
------------------------------------------------------------------------
Run baseline policy first
------------------------------------------------------------------------
'''
tax_func_path = os.path.join(
CUR_DIR, '..', 'ogusa', 'data', 'tax_functions',
'TxFuncEst_baseline_CPS.pkl') # use cached baseline estimates
kwargs = {
'output_base': base_dir,
'baseline_dir': base_dir,
'test': False,
'time_path': True,
'baseline': True,
'og_spec': og_spec,
'guid': '_example',
'run_micro': False,
'tax_func_path': tax_func_path,
'data': 'cps',
'client': client,
'num_workers': num_workers
}
start_time = time.time()
runner(**kwargs)
print('run time = ', time.time() - start_time)
'''
------------------------------------------------------------------------
Run reform policy
------------------------------------------------------------------------
'''
# update the effective corporate income tax rate
og_spec = {
'frisch': 0.41,
'start_year': START_YEAR,
'cit_rate': [0.35],
'debt_ratio_ss': 1.0,
'alpha_T': alpha_T.tolist(),
'alpha_G': alpha_G.tolist()
}
kwargs = {
'output_base': reform_dir,
'baseline_dir': base_dir,
'test': False,
'time_path': True,
'baseline': False,
'og_spec': og_spec,
'guid': '_example',
'iit_reform': iit_reform,
'run_micro': True,
'data': 'cps',
'client': client,
'num_workers': num_workers
}
start_time = time.time()
runner(**kwargs)
print('run time = ', time.time() - start_time)
# return ans - the percentage changes in macro aggregates and prices
# due to policy changes from the baseline to the reform
base_tpi = safe_read_pickle(os.path.join(base_dir, 'TPI', 'TPI_vars.pkl'))
base_params = safe_read_pickle(os.path.join(base_dir, 'model_params.pkl'))
reform_tpi = safe_read_pickle(
os.path.join(reform_dir, 'TPI', 'TPI_vars.pkl'))
reform_params = safe_read_pickle(
os.path.join(reform_dir, 'model_params.pkl'))
ans = ot.macro_table(base_tpi,
base_params,
reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['Y', 'C', 'K', 'L', 'r', 'w'],
output_type='pct_diff',
num_years=10,
start_year=og_spec['start_year'])
# create plots of output
op.plot_all(base_dir, reform_dir, os.path.join(CUR_DIR,
'run_example_plots'))
print("total time was ", (time.time() - run_start_time))
print('Percentage changes in aggregates:', ans)
# save percentage change output to csv file
ans.to_csv('ogusa_example_output.csv')
client.close()
def test_macro_table():
df = output_tables.macro_table(base_tpi, base_params, reform_tpi,
reform_params)
assert isinstance(df, pd.DataFrame)
def main():
# Define parameters to use for multiprocessing
client = Client()
num_workers = min(multiprocessing.cpu_count(), 12)
print('Number of workers = ', num_workers)
run_start_time = time.time()
reform_url = ('biden-iitax-reforms.json')
ref = Calculator.read_json_param_objects(reform_url, None)
iit_reform = ref['policy']
# Directories to save data
CUR_DIR = os.path.dirname(os.path.realpath(__file__))
base_dir = os.path.join(CUR_DIR, BASELINE_DIR)
reform_dir = os.path.join(CUR_DIR, REFORM_DIR)
'''
------------------------------------------------------------------------
Run baseline policy first
------------------------------------------------------------------------
'''
og_spec_base = {
'start_year': 2021,
'tG1': 20,
'tG2': 200,
'initial_debt_ratio': 0.982,
'debt_ratio_ss': 1.5,
'tax_func_type': 'DEP',
'age_specific': True
}
kwargs = {
'output_base': base_dir,
'baseline_dir': base_dir,
'test': False,
'time_path': True,
'baseline': True,
'og_spec': og_spec_base,
'guid': '_biden',
'run_micro': True,
'tax_func_path': None,
'data': 'cps',
'client': client,
'num_workers': num_workers
}
start_time = time.time()
runner(**kwargs)
print('run time = ', time.time() - start_time)
'''
------------------------------------------------------------------------
Run reform policy
------------------------------------------------------------------------
'''
# update the effective corporate income tax rate
og_spec_reform = {
'start_year': 2021,
'tG1': 20,
'tG2': 200,
'initial_debt_ratio': 0.982,
'debt_ratio_ss': 1.5,
'tax_func_type': 'DEP',
'age_specific': True,
'cit_rate': [0.28]
}
kwargs = {
'output_base': reform_dir,
'baseline_dir': base_dir,
'test': False,
'time_path': True,
'baseline': False,
'og_spec': og_spec_reform,
'guid': '_biden',
'iit_reform': iit_reform,
'run_micro': True,
'tax_func_path': None,
'data': 'cps',
'client': client,
'num_workers': num_workers
}
start_time = time.time()
runner(**kwargs)
print('run time = ', time.time() - start_time)
# return ans - the percentage changes in macro aggregates and prices
# due to policy changes from the baseline to the reform
base_tpi = safe_read_pickle(os.path.join(base_dir, 'TPI', 'TPI_vars.pkl'))
base_ss = safe_read_pickle(os.path.join(base_dir, 'SS', 'SS_vars.pkl'))
base_params = safe_read_pickle(os.path.join(base_dir, 'model_params.pkl'))
reform_tpi = safe_read_pickle(
os.path.join(reform_dir, 'TPI', 'TPI_vars.pkl'))
reform_ss = safe_read_pickle(os.path.join(reform_dir, 'SS', 'SS_vars.pkl'))
reform_params = safe_read_pickle(
os.path.join(reform_dir, 'model_params.pkl'))
# Macro Agg Tables
ans0 = ot.macro_table(base_tpi,
base_params,
reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['Y', 'C', 'K', 'L', 'r', 'w'],
output_type='pct_diff',
num_years=10,
start_year=2021)
ans1 = ot.macro_table(base_tpi,
base_params,
reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['Y', 'C', 'K', 'L', 'r', 'w'],
output_type='pct_diff',
num_years=10,
start_year=2031)
# Dynamic Revenue Feedback Table
ans2 = ot.dynamic_revenue_decomposition(base_params,
base_tpi,
base_ss,
reform_params,
reform_tpi,
reform_ss,
num_years=10,
include_SS=False,
include_overall=True,
start_year=2021,
table_format=None,
path=None)
# Data for Macro Aggregates Graph
ans3 = ot.macro_table(base_tpi,
base_params,
reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['Y', 'K', 'L', 'C'],
output_type='pct_diff',
num_years=60,
start_year=2021)
# Data for Debt to GDP Graph
ans4 = ot.macro_table(base_tpi,
base_params,
reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['D', 'Y'],
output_type='levels',
num_years=60,
start_year=2021)
# Data Dump
ans5 = ot.tp_output_dump_table(base_params,
base_tpi,
reform_params,
reform_tpi,
table_format=None,
path=None)
# create plots of output
op.plot_all(base_dir, reform_dir, os.path.join(CUR_DIR, 'run_biden_plots'))
print("total time was ", (time.time() - run_start_time))
print('Percentage changes in aggregates:', ans)
# save output tables to csv files
ans0.to_csv('ogusa_biden_output_2021-30.csv')
ans1.to_csv('ogusa_biden_output_2031-40.csv')
ans2.to_csv('ogusa_biden_dynamic_revenue.csv')
ans3.to_csv('ogusa_biden_macro_table.csv')
ans4.to_csv('ogusa_biden_debt2gdp_2021-30.csv')
ans5.to_csv('ogusa_biden_output_dump.csv')
client.close()
def comp_output(base_params,
base_ss,
reform_params,
reform_ss,
time_path,
base_tpi=None,
reform_tpi=None,
var='cssmat'):
'''
Function to create output for the COMP platform
'''
if time_path:
table_title = 'Percentage Changes in Economic Aggregates Between'
table_title += ' Baseline and Reform Policy'
plot1_title = 'Pct Changes in Economic Aggregates Between'
plot1_title += ' Baseline and Reform Policy'
plot2_title = 'Pct Changes in Interest Rates and Wages'
plot2_title += ' Between Baseline and Reform Policy'
plot3_title = 'Differences in Fiscal Variables Relative to GDP'
plot3_title += ' Between Baseline and Reform Policy'
out_table = ot.tp_output_dump_table(base_params,
base_tpi,
reform_params,
reform_tpi,
table_format='csv')
html_table = ot.macro_table(
base_tpi,
base_params,
reform_tpi,
reform_params,
var_list=['Y', 'C', 'I_total', 'L', 'D', 'G', 'r', 'w'],
output_type='pct_diff',
num_years=10,
include_SS=True,
include_overall=True,
start_year=base_params.start_year,
table_format='html')
fig1 = op.plot_aggregates(base_tpi,
base_params,
reform_tpi,
reform_params,
var_list=['Y', 'C', 'K', 'L'],
plot_type='pct_diff',
num_years_to_plot=50,
start_year=base_params.start_year,
vertical_line_years=[
base_params.start_year + base_params.tG1,
base_params.start_year + base_params.tG2
],
plot_title=None,
path=None)
in_memory_file1 = io.BytesIO()
fig1.savefig(in_memory_file1, format="png", bbox_inches="tight")
in_memory_file1.seek(0)
fig2 = op.plot_aggregates(base_tpi,
base_params,
reform_tpi,
reform_params,
var_list=['r_gov', 'w'],
plot_type='pct_diff',
num_years_to_plot=50,
start_year=base_params.start_year,
vertical_line_years=[
base_params.start_year + base_params.tG1,
base_params.start_year + base_params.tG2
],
plot_title=None,
path=None)
in_memory_file2 = io.BytesIO()
fig2.savefig(in_memory_file2, format="png", bbox_inches="tight")
in_memory_file2.seek(0)
fig3 = op.plot_gdp_ratio(base_tpi,
base_params,
reform_tpi,
reform_params,
var_list=['D', 'G', 'total_tax_revenue'],
plot_type='diff',
num_years_to_plot=50,
start_year=base_params.start_year,
vertical_line_years=[
base_params.start_year + base_params.tG1,
base_params.start_year + base_params.tG2
],
plot_title=None,
path=None)
in_memory_file3 = io.BytesIO()
fig3.savefig(in_memory_file3, format="png", bbox_inches="tight")
in_memory_file3.seek(0)
comp_dict = {
"renderable": [{
"media_type": "PNG",
"title": plot1_title,
"data": in_memory_file1.read()
}, {
"media_type": "PNG",
"title": plot2_title,
"data": in_memory_file2.read()
}, {
"media_type": "PNG",
"title": plot3_title,
"data": in_memory_file3.read()
}, {
"media_type": "table",
"title": table_title,
"data": html_table
}],
"downloadable": [{
"media_type": "CSV",
"title": table_title,
"data": out_table.to_csv()
}]
}
else:
table_title = 'Percentage Changes in Economic Aggregates Between'
table_title += ' Baseline and Reform Policy'
plot_title = 'Percentage Changes in Consumption by Lifetime Income'
plot_title += ' Percentile Group'
out_table = ot.macro_table_SS(base_ss,
reform_ss,
var_list=[
'Yss', 'Css', 'Iss_total', 'Gss',
'total_tax_revenue', 'Lss', 'rss',
'wss'
],
table_format='csv')
html_table = ot.macro_table_SS(base_ss,
reform_ss,
var_list=[
'Yss', 'Css', 'Iss_total', 'Gss',
'total_tax_revenue', 'Lss', 'rss',
'wss'
],
table_format='html')
fig = op.ability_bar_ss(base_ss,
base_params,
reform_ss,
reform_params,
var=var)
in_memory_file = io.BytesIO()
fig.savefig(in_memory_file, format="png", bbox_inches="tight")
in_memory_file.seek(0)
comp_dict = {
"renderable": [{
"media_type": "PNG",
"title": plot_title,
"data": in_memory_file.read()
}, {
"media_type": "table",
"title": table_title,
"data": html_table
}],
"downloadable": [{
"media_type": "CSV",
"title": table_title,
"data": out_table.to_csv()
}]
}
return comp_dict
def run_micro_macro(user_params,
reform=None,
baseline_dir=BASELINE_DIR,
reform_dir=REFORM_DIR,
guid='',
data=PUF_PATH,
ok_to_run_baseline=True):
start_time = time.time()
T_shifts = np.zeros(50)
T_shifts[2:10] = 0.01
T_shifts[10:40] = -0.01
G_shifts = np.zeros(6)
G_shifts[0:3] = -0.01
G_shifts[3:6] = -0.005
user_params = {
'frisch': 0.41,
'start_year': 2017,
'tau_b': 0.20,
'debt_ratio_ss': 1.0,
'delta_tau_annual': 0.05,
'T_shifts': T_shifts.tolist(),
'G_shifts': G_shifts.tolist()
}
'''
------------------------------------------------------------------------
Run baseline
------------------------------------------------------------------------
'''
print('path exists', not os.path.exists(baseline_dir), ok_to_run_baseline)
# if not os.path.exists(baseline_dir) and ok_to_run_baseline:
if ok_to_run_baseline:
output_base = baseline_dir
kwargs = {
'output_base': baseline_dir,
'baseline_dir': baseline_dir,
'test': False,
'time_path': True,
'baseline': True,
'analytical_mtrs': False,
'age_specific': True,
'user_params': user_params,
'guid': '',
'run_micro': False,
'small_open': False,
'budget_balance': False,
'baseline_spending': False,
'data': data,
'client': client,
'num_workers': 4
}
runner(**kwargs)
'''
------------------------------------------------------------------------
Run reform
------------------------------------------------------------------------
'''
output_base = reform_dir
kwargs = {
'output_base': output_base,
'baseline_dir': baseline_dir,
'test': False,
'time_path': True,
'baseline': False,
'analytical_mtrs': False,
'age_specific': True,
'user_params': user_params,
'guid': guid,
'reform': reform,
'run_micro': False,
'small_open': False,
'budget_balance': False,
'baseline_spending': False,
'data': data,
'client': client,
'num_workers': 4
}
runner(**kwargs)
base_tpi = safe_read_pickle(
os.path.join(baseline_dir, 'TPI', 'TPI_vars.pkl'))
base_params = safe_read_pickle(
os.path.join(baseline_dir, 'model_params.pkl'))
reform_tpi = safe_read_pickle(
os.path.join(reform_dir, 'TPI', 'TPI_vars.pkl'))
reform_params = safe_read_pickle(
os.path.join(reform_dir, 'model_params.pkl'))
ans = ot.macro_table(base_tpi,
base_params,
reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['Y', 'C', 'K', 'L', 'r', 'w'],
output_type='pct_diff',
num_years=10,
start_year=2017)
print("total time was ", (time.time() - start_time))
print('Percentage changes in aggregates:', ans)
def run_micro_macro(iit_reform, og_spec, guid, client):
guid = ''
start_time = time.time()
REFORM_DIR = os.path.join(CUR_PATH, "OUTPUT_REFORM_" + guid)
BASELINE_DIR = os.path.join(CUR_PATH, "OUTPUT_BASELINE" + guid)
tax_func_path_baseline = os.path.join(
CUR_PATH, 'TxFuncEst_baseline.pkl')
tax_func_path_reform = os.path.join(
CUR_PATH, 'TxFuncEst_policy.pkl')
# Add start year from reform to user parameters
og_spec['start_year'] = 2018
with open("log_{}.log".format(guid), 'w') as f:
f.write("guid: {}\n".format(guid))
f.write("iit_reform: {}\n".format(iit_reform))
f.write("og_spec: {}\n".format(og_spec))
'''
------------------------------------------------------------------------
Run baseline
------------------------------------------------------------------------
'''
output_base = BASELINE_DIR
kwargs = {'output_base': output_base, 'baseline_dir': BASELINE_DIR,
'test': True, 'time_path': True, 'baseline': True,
'og_spec': og_spec, 'run_micro': False,
'tax_func_path': tax_func_path_baseline, 'guid': guid,
'client': client, 'num_workers': NUM_WORKERS}
runner(**kwargs)
'''
------------------------------------------------------------------------
Run reform
------------------------------------------------------------------------
'''
output_base = REFORM_DIR
kwargs = {'output_base': output_base, 'baseline_dir': BASELINE_DIR,
'test': True, 'time_path': True, 'baseline': False,
'iit_reform': iit_reform, 'og_spec': og_spec,
'guid': guid, 'run_micro': False,
'tax_func_path': tax_func_path_reform,
'client': client, 'num_workers': NUM_WORKERS}
runner(**kwargs)
time.sleep(0.5)
base_tpi = safe_read_pickle(
os.path.join(BASELINE_DIR, 'TPI', 'TPI_vars.pkl'))
base_params = safe_read_pickle(
os.path.join(BASELINE_DIR, 'model_params.pkl'))
reform_tpi = safe_read_pickle(
os.path.join(REFORM_DIR, 'TPI', 'TPI_vars.pkl'))
reform_params = safe_read_pickle(
os.path.join(REFORM_DIR, 'model_params.pkl'))
ans = ot.macro_table(
base_tpi, base_params, reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['Y', 'C', 'K', 'L', 'r', 'w'], output_type='pct_diff',
num_years=10, start_year=og_spec['start_year'])
print("total time was ", (time.time() - start_time))
return ans
def main():
# Directories to save data
CUR_DIR = os.path.dirname(os.path.realpath(__file__))
base_dir = os.path.join(CUR_DIR, BASELINE_DIR)
reform_dir = os.path.join(CUR_DIR, REFORM_DIR)
# return ans - the percentage changes in macro aggregates and prices
# due to policy changes from the baseline to the reform
base_tpi = safe_read_pickle(
os.path.join(base_dir, 'TPI', 'TPI_vars.pkl'))
base_ss = safe_read_pickle(
os.path.join(base_dir, 'SS', 'SS_vars.pkl'))
base_params = safe_read_pickle(
os.path.join(base_dir, 'model_params.pkl'))
reform_tpi = safe_read_pickle(
os.path.join(reform_dir, 'TPI', 'TPI_vars.pkl'))
reform_ss = safe_read_pickle(
os.path.join(reform_dir, 'SS', 'SS_vars.pkl'))
reform_params = safe_read_pickle(
os.path.join(reform_dir, 'model_params.pkl'))
# Macro Agg Tables
ans0 = ot.macro_table(
base_tpi, base_params, reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['Y', 'C', 'K', 'L', 'r', 'w'], output_type='pct_diff',
num_years=10, start_year=2021)
ans1 = ot.macro_table(
base_tpi, base_params, reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['Y', 'C', 'K', 'L', 'r', 'w'], output_type='pct_diff',
num_years=10, start_year=2031)
# Dynamic Revenue Feedback Table
ans2 = ot.dynamic_revenue_decomposition(
base_params, base_tpi, base_ss, reform_params, reform_tpi, reform_ss,
num_years = 10, include_SS=False, include_overall=True,
start_year=2021, table_format=None, path=None)
# Data for Macro Aggregates Graph
ans3 = ot.macro_table(
base_tpi, base_params, reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['Y', 'K', 'L', 'C'], output_type='pct_diff',
num_years=60, start_year=2021)
# Data for Debt to GDP Graph
ans4 = ot.macro_table(
base_tpi, base_params, reform_tpi=reform_tpi,
reform_params=reform_params,
var_list=['D','Y'], output_type='levels',
num_years=60, start_year=2021)
# Data Dump
ans5 = ot.tp_output_dump_table(
base_params, base_tpi, reform_params,
reform_tpi, table_format=None, path=None)
# create plots of output
op.plot_all(base_dir, reform_dir,
os.path.join(CUR_DIR, 'run_biden_plots'))
# save output tables to csv files
ans0.to_csv('ogusa_biden_output_2021-30.csv')
ans1.to_csv('ogusa_biden_output_2031-40.csv')
ans2.to_csv('ogusa_biden_dynamic_revenue.csv')
ans3.to_csv('ogusa_biden_macro_table.csv')
ans4.to_csv('ogusa_biden_debt2gdp_2021-30.csv')
ans5.to_csv('ogusa_biden_output_dump.csv')
client.close()